Learning Abilities and Disabilities: Generalist Genes, Specialist Environments

ABSTRACT— Twin studies comparing identical and fraternal twins consistently show substantial genetic influence on individual differences in learning abilities such as reading and mathematics, as well as in other cognitive abilities such as spatial ability and memory. Multivariate genetic research has shown that the same set of genes is largely responsible for genetic influence on these diverse cognitive areas. We call these "generalist genes." What differentiates these abilities is largely the environment, especially nonshared environments that make children growing up in the same family different from one another. These multivariate genetic findings of generalist genes and specialist environments have far-reaching implications for diagnosis and treatment of learning disabilities and for understanding the brain mechanisms that mediate these effects.     

大学新生主观社会地位与抑郁的变化轨迹:一项多元潜变量增长模型分析

采用整群取样的方法在全国按东部、中部和西部共抽取了7所高校的1983名新生,进行了历时四个月的四次追踪测试。使用潜变量增长模型建模,分别考察了新生主观社会地位和抑郁的变化轨迹,并就两者间的变化关系进行了分析。结果发现新生入学后四个月内：（1）主观社会地位呈阶段化线性增长,其起始水平和第一阶段的增长速度存在显著个体间差异;（2）抑郁呈二次方增长,其起始水平和增长速度存在显著个体间差异;（3）入学后主观社会地位的下滑速度能有效预测新生抑郁水平的上升速度。研究基于情绪压制理论,对主观社会地位与抑郁间的变化关系进行了分析。     

The integration of multidimensional scaling and multivariate analysis with optimal transformations

The recent history of multidimensional data analysis suggests two distinct traditions that have developed along quite different lines. In multidimensional scaling (MDS), the available data typically describe the relationships among a set of objects in terms of similarity/dissimilarity (or (pseudo-)distances). In multivariate analysis (MVA), data usually result from observation on a collection of variables over a common set of objects. This paper starts from a very general multidimensional scaling task, defined on distances between objects derived from one or more sets of multivariate data. Particular special cases of the general problem, following familiar notions from MVA, will be discussed that encompass a variety of analysis techniques, including the possible use of optimal variable transformation. Throughout, it will be noted how certain data analysis approaches are equivalent to familiar MVA solutions when particular problem specifications are combined with particular distance approximations.This research was supported by the Royal Netherlands Academy of Arts and Sciences (KNAW). An earlier version of this paper was written during a stay at McGill University in Montréal; this visit was supported by a travel grant from the Netherlands Organization for Scientific Research (NWO). I am grateful to Jim Ramsay and Willem Heiser for their encouragement and helpful suggestions, and to the Editor and referees for their constructive comments.